A Dual-Stage Deep Learning Framework for Weed Detection

Weed detection is a critical challenge in precision agriculture, where accurate identification of weed boundaries can significantly enhance targeted herbicide application and sustainable farming practices. This study proposes a novel hybrid approach that integrates YOLO for real-time object detection with U-Net for pixel-wise segmentation, leveraging the strengths of both techniques. YOLO efficiently identifies weed regions, providing bounding boxes around detected weeds, while U-Net refines these detections by accurately delineating the boundaries of the weeds within the detected regions. The proposed method was evaluated on a publicly available dataset, and experimental results demonstrated its robustness across varying weed sizes, shapes, and environmental conditions. The hybrid system outperformed standalone object detection by addressing its limitations in boundary precision, showcasing its potential for precise weed management. This work represents a significant step toward more efficient and reliable automated weed detection systems, contributing to the advancement of precision agriculture.